code: def test_multivariate_normal_dist_kl_div_batch():
      n_classes = 2
      dist_a = MultivariateNormalDist(
          num_var=n_classes,
          mean=mx.nd.array([[0.2, 0.8], [0.2, 0.8]]),
          sigma=mx.nd.array(
              [[[1, 0.5], [0.5, 0.5]], [[1, 0.5], [0.5, 0.5]]]
          ),
      )
      dist_b = MultivariateNormalDist(
          num_var=n_classes,
          mean=mx.nd.array([[0.3, 0.7], [0.3, 0.7]]),
          sigma=mx.nd.array(
              [[[1, 0.2], [0.2, 0.5]], [[1, 0.2], [0.2, 0.5]]]
          ),
      )
  
      actual = dist_a.kl_div(dist_b).asnumpy()
      np.testing.assert_almost_equal(
          actual=actual, desired=[0.195100128, 0.195100128]
      )
  
imports: import mxnet
  import numpy
  import os
  import pytest
  from scipy import stats
  import sys
  from rl_coach.architectures.head_parameters import PPOHeadParameters
  from rl_coach.architectures.mxnet_components.heads.ppo_head import CategoricalDist, MultivariateNormalDist, DiscretePPOHead, ClippedPPOLossDiscrete, ClippedPPOLossContinuous, PPOHead
  from rl_coach.agents.clipped_ppo_agent import ClippedPPOAlgorithmParameters, ClippedPPOAgentParameters
  from rl_coach.spaces import SpacesDefinition, DiscreteActionSpace
label_desc: Use image node_id 5 for calling a global function with example usage: test_multivariate_normal_dist_kl_div_batch() without return types